Title

Detecting Communities Using Bibliographic Metrics

Keywords

Community discovery/identification; Graph clustering

Abstract

We propose an efficient and novel approach for discovering communities in real-world random networks. Communities are formed by subsets of nodes in a graph, which are closely related. Extraction of these communities facilitates better understanding of such networks. Community related research has focused on two main problems: community discovery and community identification. Community discovery is the problem of extracting all the communities in a given network where as community identification is the problem of identifying the community to which a given set of nodes from the network belong. In this paper we first give a brief survey of the existing community-discovery algorithms and then propose a novel algorithm to discovering communities using bibliographic metrics. We also test the proposed algorithm on real-world networks and on computer-generated models with known community structures. © 2006 IEEE.

Publication Date

11-22-2006

Publication Title

2006 IEEE International Conference on Granular Computing

Number of Pages

293-298

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

33751092543 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/33751092543

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